A broadband method of quantifying phase synchronization for discriminating seizure EEG signals

BackgroundAutism spectrum disorders (ASD) and intellectual disabilities (ID) are heterogeneous and complex developmental diseases with significant genetic backgrounds and overlaps of genetic susceptibility loci. Copy number variants (CNVs) are known to be frequent causes of these impairments. However, the clinical heterogeneity of both disorders causes the diagnostic efficacy of CNV analysis to be modest. This could be resolved by stratifying patients according to their clinical features.AimFirst, we sought to assess the significance of particular clinical features for the detection of pathogenic CNVs in separate groups of ID and ASD patients and determine whether and how these groups differ from each other in the significance of these variables. Second, we aimed to create a statistical model showing how particular clinical features affect the probability of pathogenic CNV findings.MethodWe tested a cohort of 204 patients with ID (N = 90) and ASD (N = 114) for the presence of pathogenic CNVs. We stratified both groups according to their clinical features. Fisher’s exact test was used to determine the significance of these variables for pathogenic CNV findings. Logistic regression was used to create a statistical model of pathogenic CNV findings.ResultsThe frequency of pathogenic CNV was significantly higher in the ID group than in the ASD group: 18 (19.78%) versus 8 (7%) (p < 0.004). Microcephaly showed a significant association with pathogenic findings in ID patients (p < 0.01) according to Fisher’s exact test, whereas epilepsy showed a significant association with pathogenic findings in ASD patients (p < 0.01). The probability of pathogenic CNV findings when epilepsy occurred in ASD patients was more than two times higher than if epilepsy co-occurred with ID (29.6%/14.0%). Facial dysmorphism was a significant variable for detecting pathogenic CNVs in both groups (ID p = 0.05, ASD p = 0.01). However, dysmorphism increased the probability of pathogenic CNV detection in the ID group nearly twofold compared to the ASD group (44.4%/23.7%). The presence of macrocephaly in the ASD group showed a 25% probability of pathogenic CNV findings by logistic regression, but this was insignificant according to Fisher’s exact test. The probability of detecting pathogenic CNVs decreases up to 1% in the absence of dysmorphism, macrocephaly, and epilepsy in the ASD group.ConclusionDysmorphism, microcephaly, and epilepsy increase the probability of pathogenic CNV findings in ID and ASD patients. The significance of each feature as a predictor for pathogenic CNV detection differs depending on whether the patient has only ASD or ID. The probability of pathogenic CNV findings without dysmorphism, macrocephaly, or epilepsy in ASD patients is low. Therefore the efficacy of CNV analysis is limited in these patients.

The aim of this article is to discuss the neuronal substrates of sleep oscillations leading to seizures consisting of spike-wave (SW) complexes at 2-4 Hz, mimicking those seen in absence epilepsy, or SW and polyspike-wave (PSW) complexes at 1.5-2.5 Hz, often associated with fast runs at 10-15 Hz, as in the Lennox-Gastaut syndrome. Extracellular recordings were done in permanently implanted animals during the natural waking-sleep cycle. Single and dual simultaneous recordings from cortical neurons, cortical and thalamic neurons, or cortical neurons and glial cells were performed in cats under ketamine-xylazine anesthesia. (a) The minimal substrate of SW seizures is the neocortex because such seizures may occur in thalamectomized animals, in which spindles are absent. In intact-brain animals, SW seizures are initiated in neocortex and spread to the thalamus after a few seconds. The majority of thalamocortical (TC) neurons are steadily hyperpolarized throughout the cortical SW seizures. (b) In the Lennox-Gastaut syndrome, the paroxysmal depolarizing shifts (PDSs) associated with the EEG "spike" of SW/PSW complexes contain an important inhibitory component, whereas the hyperpolarization during the EEG "wave" component is not due to gamma-aminobutryic acid (GABA)ergic inhibitory postsynaptic potentials (IPSPs) but is ascribed to a mixture of disfacilitation and K+ currents. As is also the case with seizures consisting of pure SW complexes, the majority of TC neurons are hyperpolarized during the cortical paroxysms and disinhibited after the cessation of cortical seizures. Seizures with SW complexes and of the Lennox-Gastaut type preferentially evolve from sleep oscillations. They are initiated in neocortex and spread to the thalamus after a few seconds. The majority of TC neurons are inhibited during these seizures.

Rhythmic 3–4 Hz discharge is insufficient to produce cortical BOLD fMRI decreases in generalized seizures

Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide. The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram (EEG) as a noninvasive procedure to record neuronal activities in the brain. EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals. Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform. Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification. Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector. The accuracy of the proposed approach is higher for Q=2 and J=10. Transfer entropy is observed to be significant for different class combinations. Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time. The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals. The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.

This paper aims to analyze the optimization of Epilepsy risk levels from EEG signals using Fuzzy based Elman-Chaotic Optimization. The EEG (Electroencephalogram) signals of twenty patients are collected from Sri Ramakrishna Hospitals at Coimbatore. The raw EEG signals are sampled and various parameters like energy, energy, variance, peaks, sharp and spike waves, duration, events and covariance. The fuzzy techniques are applied as a first level classifier to classify the risk levels of epilepsy by converting the EEG signal parameters in to code patterns by fuzzy systems. Elman-Chaotic optimization is identified as post classifiers on the classified data to obtain the optimized risk level that characterizes the patient’s epilepsy risk level. This classification provides a better way of treating the epileptic patients. This project aims to safeguard a patient’s life when critical situation occurs. Future scope is to design an embedded system which collects the raw EEG signals from the brain and directly gives the level of epilepsy. It will make the neural surgeons to give appropriate remedial measures.

Epilepsy prediction in epileptic patients are considered as the most crucial issue since decades. According to the world health organization (WHO) around fifty million people are suffering from epilepsy which is caused by the neurological disorder. Sudden and abrupt seizures are acknowledged as epilepsy. Seventy percent of the epileptic patients belong to the lower and middle-class countries. Seizures are very complex to predict as they are very sudden and it is very hard to discriminate the neurological signals. Seizures are due to the redundant discharge of electrical signals from brain cells. Several artificial intelligence based and deep learning based methodologies have been applied to detect seizures as they are dangerous to the growth of the brain. Classification and patterns of brain signals were classified using electroencephalogram (EEG) signals. Magnetic resonance imaging (MRI) of brain has also been performed to identify the flash floods. The identification of seizures in hospitals is acknowledged as expensive as the EEG signals and MRI testing equipment are very costly. According to the WHO mostly the epileptic patients belong to the poor countries therefore a cost-effective solution is needed. Seventy percent of the epileptic patients can live a normal life if it was properly diagnosed and treated. In this research paper a novel cost-effective solution has been presented for the vigorous prediction of the seizures in epileptic patients. Several methodologies and case studies have been studied by researchers but cannot be acknowledged as cost-effective as researchers used EEG and MRI signals. In this research paper a cost-effective hardware model has been designed and Modified cuckoo search has been applied to predict the seizures. Results have proved that the proposed research can be acknowledged as cost effective solution with higher accuracy and less false alarm rate.

Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude-frequency modulated (AM-FM) components. The statistical measures of AM-FM components used as input features to least squares support vector machine classifier for classification of seizure and seizure-free EEG signals. The proposed methodology is evaluated on a benchmark epileptic seizure EEG database. The experimental results demonstrate that the proposed scheme has capability to effectively distinguish seizure and seizure-free EEG signals.

Using simulated neuronal cell models for detection of epileptic seizures in foramen ovale and scalp EEG

Phase synchrony analysis of EEG during music perception reveals changes in functional connectivity due to musical expertise

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high- and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

Automatic epileptic seizure detection technologies for clinical diagnosis mainly rely on electroencephalogram (EEG) recordings, which are immensely useful tools for epileptic location and identification. Currently, traditional seizure detection methods based only on single-view features have great limitations for the typical dynamic and nonlinear EEG signals. An objective of this paper is to investigate the effect of multiview feature selection and multilevel spectral analysis methods on the identification of the EEG signals for seizure detection. Here, multiview features are extracted from time domain, frequency domain, and information theory to collect adequate information of EEG signals. And a feature selection algorithm based on particle swarm optimization (PSO) is proposed for automatic seizure detection. Moreover, due to the different frequency components of the EEG signals, they are divided into four kinds of brain waves for multilevel spectral analysis. The effect of these four rhythm waves on seizure detection is compared. Three well-known classifiers are employed to classify EEG signals concerning seizure or nonseizure events. The result shows that the average accuracy, specificity, and sensitivity of classification with the CHB-MIT database are 98.14%, 98.64%, and 96.79%, respectively. The application of the PSO-based feature selection method for automatic seizure detection improves accuracy by 5.99% with the SVM classifier. Compared with the state-of-the-art methods, the proposed method has superior competence with high performance for automatic seizure detection. It is further shown that the feature selection method is an indispensable step in seizure detection. With PSO-based feature selection and multilevel spectral analysis, the θ wave in the frequency range of 4-7 Hz shows better performance in the identification of EEG signals and is more suitable for the proposed method. The PSO-based feature selection algorithm for automatic seizure detection can be a useful assistant tool for clinical diagnosis.

Emotion influences human life and impacts daily life activities. The central nervous system (CNS) and peripheral nervous system (PNS) interact intricately to produce emotional experiences. There are various studies related to the interaction of the CNS-PNS during psychophysiological states. However, emotion-oriented, CNS-PNS phase synchronisation studies are limited. In this work, an attempt is made to investigate the interaction between the CNS and PNS using electroencephalogram (EEG) and photoplethysmogram (PPG) during emotional elicitation evoked by audio–visual stimuli. For this, EEG and PPG signals from the publicly accessible DEAP database are utilized. EEG signals are segmented into four frequency bands: theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–45 Hz). Subsequently, the instantaneous phases of EEG and PPG are computed and employed to determine the phase synchronization index (PSI). Finally, the Wilcoxon Rank-sum test is performed to estimate the statistical significance of the PSI in low verus high levels of arousal and valence emotional states. Results show that the theta band of the EEG has the highest phase synchronization with the PPG signals. In low arousal, there's higher phase synchronization, whereas high valence exhibits higher phase synchronization. The FC5, C3, PO3, and F4 electrodes are found to be statistically significant (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.01) for arousal. For valence PO4 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.01) is found to be statistically significant in discriminating valence levels. It is also evident that PSI based on the theta and alpha bands can distinguish levels of arousal and valence. Hence, the proposed approach can be used to better understand the CNS-PNS interaction in various emotional states under clinical conditions.

Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information about its activities. In this paper, we propose a new approach for the early detection of epileptic seizure in EEG. The proposed approach is based on Discrete Wavelet Transform (DWT) and Ant Colony (AC) Classifier. We started by applying DWT to decompose the EEG signal into its sub-bands to extract the energy ratio from wavelet coefficients. Beside we extract some statistical features from the original signal, and we use the extracted features as the input for the AC algorithm to derive classification rules, which are used to detect epileptic seizures in the EEG of the monitored patient. Our experimental results on real dataset show that our proposed approach achieves a high level of detection accuracy.

BackgroundAnnual health checks for adults with intellectual disability (ID) have been incentivised by NHS England since 2009, but it is unclear what impact they have had on important health outcomes...

With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. Graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children’s Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%.